{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_len = 12\n",
    "seq_len = 288\n",
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/complementation_result/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_mae(word_len=1, comple_len=12):\n",
    "    file = folder_path + f'Complement_Result{word_len}_{comple_len}.csv'\n",
    "    df = pd.read_csv(file)\n",
    "    num_seq = len(df['Input288']) / seq_len\n",
    "    input_list = []\n",
    "    complement_list = []\n",
    "\n",
    "    for num in range(int(num_seq)):\n",
    "        start = int(num * seq_len)\n",
    "        end = int(num * seq_len + seq_len)\n",
    "        input288 = [input for input in df['Input288'].iloc[start:end].tolist()]\n",
    "        input_list.append(input288)\n",
    "\n",
    "        position288 = [pos for pos in df['Position'].iloc[start:end].tolist()]\n",
    "        comple288 = []\n",
    "        for ind, pos in enumerate(position288):\n",
    "            comple = df['Complementation'].iloc[start+ind] if pos==1.0 else input288[ind]\n",
    "            comple288.append(comple)\n",
    "        complement_list.append(comple288)\n",
    "        \n",
    "    mae_list = []\n",
    "    for i in range(len(input_list)):\n",
    "        mae = mean_absolute_error(input_list[i], complement_list[i])\n",
    "        mae_list.append(mae)\n",
    "    return mae_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9192 0.01608356597222223\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "76",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m/Users/gengyunxin/opt/anaconda3/envs/pytorch1.6.0/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2898\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 76",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-ee061852a9e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mcomplement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Complementation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mday_num\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mseq_len\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mday_num\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mseq_len\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mcomplement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1.0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Truth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gengyunxin/opt/anaconda3/envs/pytorch1.6.0/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 882\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gengyunxin/opt/anaconda3/envs/pytorch1.6.0/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/gengyunxin/opt/anaconda3/envs/pytorch1.6.0/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2898\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2899\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2900\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2902\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 76"
     ]
    }
   ],
   "source": [
    "word_len = 1\n",
    "comple_len = 12\n",
    "file = folder_path + f'Complement_Result{word_len}_{comple_len}.csv'\n",
    "df = pd.read_csv(file)\n",
    "\n",
    "mae1 = np.array(cal_mae(word_len=1, comple_len=12))\n",
    "# print(len(mae1)) # 22720\n",
    "min_index, min = mae1.argmin(), mae1.min()\n",
    "print(min_index, min)\n",
    "\n",
    "day_num = min_index\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len]\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len]\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len]\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22720\n",
      "14415 0.028629923611111077\n"
     ]
    }
   ],
   "source": [
    "mae2 = np.array(cal_mae(word_len=2))\n",
    "print(len(mae2)) # 22720\n",
    "min_index, min = mae2.argmin(), mae2.min()\n",
    "print(min_index, min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22720\n",
      "9930 0.015382132291666665\n"
     ]
    }
   ],
   "source": [
    "mae6 = np.array(cal_mae(word_len=6))\n",
    "print(len(mae6)) # 22720\n",
    "min_index, min = mae6.argmin(), mae6.min()\n",
    "print(min_index, min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22720\n",
      "12182 0.01623233333333333\n"
     ]
    }
   ],
   "source": [
    "mae12 = np.array(cal_mae(word_len=12))\n",
    "print(len(mae12)) # 22720\n",
    "min_index, min = mae12.argmin(), mae12.min()\n",
    "print(min_index, min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22720\n",
      "12426 0.00901110798611112\n"
     ]
    }
   ],
   "source": [
    "mae24 = np.array(cal_mae(word_len=24))\n",
    "print(len(mae24)) # 22720\n",
    "min_index, min = mae24.argmin(), mae24.min()\n",
    "print(min_index, min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
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       "      <td>41.545097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6543360 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Complementation   Input288  Input48  Position\n",
       "0                 0        80.014560  80.723785      0.0       0.0\n",
       "1                 1        77.927780  79.936485      0.0       0.0\n",
       "2                 2        77.242230  77.447070      0.0       0.0\n",
       "3                 3        75.638084  73.705890      0.0       0.0\n",
       "4                 4        74.253370  68.421580      0.0       0.0\n",
       "...             ...              ...        ...      ...       ...\n",
       "6543355     6543355        62.336000  46.619910      0.0       0.0\n",
       "6543356     6543356        61.351673  45.349926      0.0       0.0\n",
       "6543357     6543357        59.794216  43.624940      0.0       0.0\n",
       "6543358     6543358        59.411045  42.854115      0.0       0.0\n",
       "6543359     6543359        58.757397  41.545097      0.0       0.0\n",
       "\n",
       "[6543360 rows x 5 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = folder_path + f'Complement_Result{word_len}_{comple_len}.csv'\n",
    "df = pd.read_csv(file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd337149d30>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rYXVmEfPHR/PDw+ez4t7JAPzjqyy+zTrCTRMH8uz8JNLzynl3c9Mw4rc25jI4zJdJg0IAmi9cm0yKRVPj2Li/lJtf3dxtdRSO0+5vqlIqWim1Vim1Sym1Uyn1M2v5EqVUgVIqzfrvkhb7PKyUylZK7VFKze5MYF5eXpSWlrp0UhAdo7WmtLS0Q8NJndG6vcUUlJ/gstGR3fq+Ad7uXDW2Px9szeeG/2zi+a+zTnn9852H0RpuPy+WMH8v+gf24fwR4SxLyafRorlqbH8uHhnJ+Jgg/vNdDrsPHyM9v4IbzxlwxtBjgDumxPGbS4azOadMFpnvATrS7dMA/FJrvU0p5QdsVUqtsb72tNb6yZYbK6XigflAAtAP+FIpNVRr3WhLYFFRUeTn51NSUmLLbsJFeXl5ueRNX3ll1Tz3dTaRAV5cMrJ7kz80JeRvs0pwN5l4bUMud0yNw996QXZVeiFDwnxPmU76t3PiGTMgkP6BfRgc1lR+57RB3P5GCr9dsROgzXH914yL5q+f7+Hj1AJGRPaeYbw9UbvJX2tdCBRaHx9XSu0C+rexyxXAUq11LZCjlMoGJgAbbQnM3d2d2NhYW3YRoluVVtZy8bPfUVnbwJ+uHGn3m606IjbUh+9+NZMdBRXMeW49j6/axRNXjySz8Bibc8tYfPHwU7aPDvbm7umDTymbPiyMCH8vNueUMTDEu827hoN9PJg+rC+vfZ9LyfFa/n7t6Fa/JQjnZ9Nvq1IqBhgDbLIW3auUylBKvaqUCrKW9QfyWuyWTysfFkqpRUqpFKVUirTuhSvalFNGZW0Db942gevPGWBoLIn9A7h3xmDeS8lj6P99xo3/2YS3h5kF49uPy2xSzB3T9CfakXsUfn9FIhckhLM8tYBt1lXGWq5hIFxDh5O/UsoX+BB4QGt9DPgXMAhIoumbwd9PbtrK7md03GutX9JaJ2utk/v27b7hcULYyw/7S/H2MHOu9eKo0X5xwVCWXBbPzefG4OPpxh3nxRLg3bEx+fOSo/BwMzE7of2bFfsF9uGJq0bi4WZi+bZ8Hl6+naTHVrMq41BXqyC6UYeGeiql3GlK/G9rrZcDaK2LWrz+MnByXuJ8ILrF7lGA/FaIHueH/aWMGxhkSHdPa0wmxcLJTV2lj86Jt2nfQX19yfjdhR0ewunn5c6MYX15e1PTKKF+AV78clk6BUdPMHZgEGMHBDlsigthH+0mf9XUofcKsEtr/VSL8kjr9QCAK4Ed1scrgXeUUk/RdMF3CCBjw0SPoLVmX0klfl7u7C2q5Iqkti5/uRZbx+4vmjqIhkbNLZNiSOjnz8+XpfPnz5pmp33muqTmriThnDrS8p8M3ARsV0qlWct+AyxQSiXR1KWTC/wUQGu9Uym1DMikaaTQPbaO9BHCWX2UWsAvlqUzNNwXpTBkhI+zGDcwiFcWjm9+/uZtE8gqOs6VL2xg28GjkvydXEdG+6yn9X78T9vY53Hg8S7EJYThdhRUsCW3jIWTYmi0aNLzK3ju66ZZLvcWVXJxYgSxoT4GR+lchoT7kdDPn/T8CqNDEe2Q6R2EaEVDo4X7l6ayv6SKomO1RPh7suSTTAAeuWQE32aV8PMLhhocpXMaHR3I6xtyqWuw4OFmQmvNTa9sZkSkH49catu1COE4kvyFaMW7mw+yv6SKsQMCefGbfYT6ehAf6c+vLx7O1CGh/GRqnNEhOq2R/QOoa7Cwt+g4if0D2LivlPXZR0jPK+eXFw6TeYGchCR/IU6zt+g4f/p0N5MGhfCfW5KZ8eQ6io7V8tDsYUwbKsOS23NykrmrXthAZKAXNfWNuJkUx2sb+GZvSYeGkwrHc44xakI4kf/7eAfeHmaeuS4Jbw83Hrs8kVFRAcwZ1c/o0FxCdLA3z85P4tbzYhjZP4B+gX3449xEgn08WJFWYHR4wkpa/kK0kJZXzuacMh6dE0+Yf9NEcxclRnBRorRWbXFFUv8zhsFmF1fy2oZcCitOEBnQx6DIxEnS8heihde/z8HPy43rxke3v7Gwyc3nxmDRmv/+cMDoUASS/IU4xcb9pcwcHoavp3wptrcBId7MHBbGh1sLZKp2JyDJXwiromM1FB2rZXRUoNGh9FiXjIzk8LEathfIfQBGk+QvhFV6XjkAo6MDjA2kB5s5PAyzSfHFzsNGh9LrSfIXwiojvwKzSZHQT5K/owT5eDAhJphPtx+Wrh+DSfIXwmrrgaMMC/eTm5AcbF5yFDlHqliffYT8o9Us25LX/k7C7iT5C0HTEM+N+0u5MOHsSxgK+7h0VCShvh689O1+Hnw/nV99mEFB+Qmjw+p1JPmLXq2h0UJtQyOPfbKTUF8P7pgi0zY4mqebmTunDeK7rCP8sL8MgC05ZQZH1fvIeDbRa+WVVXPlCxuobWjkeE0Dzy0YI0M8u8nt58WilGLbwaN8u6eEzbllMgV0N5PfdNEr7T58jPveSaWuoZHxMcGcPyKMy0bL9A3dRSnF7efFcjux3PLqZmn5G0C6fUSPt3FfKVc8v569RccBSD14lMuf+56SylpeuGEcry4czw3nDDQ4yt5rQmwwWcWVFB2rMTqUXkVa/qJHO15Tzy+XpXGoooZr/rUBrZuWnuvr58nKeycT4utpdIi93iUjI/nbF3tYtiWP+84fYnQ4vYa0/IXhdh8+xq7CY3Y/rtaa//t4B4XHavjDFQkMj/Tn4pERjOwfwL9uHCuJ30nEhvowZUgo724+SEOjxehweg1p+QtDlVfXccPLm/D1cmPdg9NRqrUVQ233yvoc/vL5buoaLPzygqHcdG4MN50bY5djC/u75dwY7ngzhdc35MqIq24iLX9hqL+v3ktpVR0HSqvZVXjcLsesqm3gua+zGNzXl/+7dAT3zBhsl+MKxzl/RBizRoTzty/2cLhC+v67gyR/YaivdhUxaVAIJgWf7yjs0D4fpeZzxfPr+ST9UKuvv/Z9DuXV9Tx+ZSJ3TInDZLLPtwnhOEopfnHBUGobLGzYd8TocHoFSf7CMFW1DRyqqGHy4FDGxwTz9Z7idvfJK6vm5++lk1l4jCUrd3Kspr75tfLqOpZuPsizX2VxcWIEYwYEOTJ8YWfDIvzw9XRj28GjRofSK0jyb4PZbCYpKYmEhARGjx7NU089hcVi3wtSf/7znxk8eDDDhg3jiy++sOuxnd2+kkoABvX1ZUSkP7lHqtud7GvrgabE8Me5iZRV1/HcV1lA08XdG/6zicXLt9MvsA9PXDXKscELuzObFKOjA0g9WG50KL2CXPBtQ58+fUhLSwOguLiY66+/noqKCh577DG7HD8zM5OlS5eyc+dODh06xKxZs9i7dy9mc++YWCy7uCn5Dw7zJf9oNZW1DVScqCfQ2+Os+2w7eBQfDzNXj40iJfcor2/I5fpzBrK36Dg7Dx3jd5fFs2DCAJmczUWNiQ7iX9/so7quAW8PSU+OJC3/DgoLC+Oll17i+eefR2tNbm4uU6ZMYezYsYwdO5YNGzYAcNNNN7FixYrm/W644QZWrlzZ6jFXrFjB/Pnz8fT0JDY2lsGDB7N58+ZuqY8zyC6uxM2kGBjiTVSQNwD5R9ue4GvrgaOMjg7EzWzioYuG4WE28cyXe/nn2mxiQry5aeJASfwubOzAQBotuvkbnnCcdpO/UipaKbVWKbVLKbVTKfUza3mwUmqNUirL+jOoxT4PK6WylVJ7lFKzHVmB7hQXF4fFYqG4uJiwsDDWrFnDtm3beO+997j//vsBuOOOO3jttdcAqKioYMOGDVxyySWtHq+goIDo6B/Xio2KiqKgoMDxFXESWcWVxIT64G42ER3ctKB3Xln1Wbevrmtg9+HjjBvY9KsW5ufFvORoPkk/REZ+BbedF4ubWdozruzcuFC8Pcx8ur1jF/9F53XkL6UB+KXWegQwEbhHKRUPLAa+0loPAb6yPsf62nwgAbgIeEEp1WOaYif7pOvr6/nJT37CyJEjmTdvHpmZmQBMmzaN7OxsiouLeffdd7n66qtxc2v962tr/dv2GufuTKrrGrj3nW3NffwnZRUdZ3BfX4AOtfyziipptOhTFlu5ceJALBp8PMxcKRODubw+HmZmJ0Twv4xCahsajQ6nR2s3+WutC7XW26yPjwO7gP7AFcAb1s3eAOZaH18BLNVa12qtc4BsYIKd4zbE/v37MZvNhIWF8fTTTxMeHk56ejopKSnU1dU1b3fTTTfx9ttv89prr3Hrrbee9XhRUVHk5f24kEV+fj79+vW8ycU27S9jVUYhT63e21x2tKqO3NJqRlmXTAzo446flxv5R8/e8s8trQIgrq9Pc9ngMF+uP2cA984cgp+Xu4NqILrTFUn9OFbTwB9X7aJe7vh1GJu+IyulYoAxwCYgXGtdCE0fEECYdbP+QMulefKtZacfa5FSKkUplVJSUtKJ0LtXSUkJd955J/feey9KKSoqKoiMjMRkMvHWW2/R2PhjK2XhwoU888wzACQkJJz1mJdffjlLly6ltraWnJwcsrKymDChR3xOniLVOnTvsx2FvP59DhUn6knLLweaLvCdFBXkTV4bLf/9JVUoBQOCvU8p/9OVI7lr+iD7By4MMXVIX26dHMNbPxxg+bZ8o8PpsTp8OV0p5Qt8CDygtT7WRvdEay+c0b+htX4JeAkgOTnZKRfzPHHiBElJSdTX1+Pm5sZNN93EL37xCwDuvvturr76at5//31mzJiBj8+PrdHw8HBGjBjB3Llz2zx+QkIC1157LfHx8bi5ufHPf/6zR470Sc0rp39gHyxas+STTP63vZDkmGBMCkZF/diFEx3Uh+zTuoZayi2tol9AH7mg28OZTIrfzonnk/RCNu0v47rxA4wOqUfqUPJXSrnTlPjf1lovtxYXKaUitdaFSqlI4OQdOvlAdIvdo4DWb8V0ci1b86cbMmQIGRkZzc///Oc/Nz+urq4mKyuLBQsWtPsejzzyCI888kjXAnViFosmLa+cOaP68ce5iaxIK+AXy9LZknuUmBBvfFosnnLuoBBWZxaxJrOIC+LPXE4x90gVsaE+Z5SLnkcpxbiBgWyVG74cpiOjfRTwCrBLa/1Ui5dWArdYH98CrGhRPl8p5amUigWGAL1m/OKXX37J8OHDue+++wgICGh/hx4up7SK4zUNjIkOxGxSXDU2isevTAQ4I8HfOHEgQ8N9WbJyJyfqfvzg1VqzcV8pe4sqJfn3IuMGBnGgtJojlbVGh9IjdaTlPxm4CdiulEqzlv0GeAJYppS6HTgIzAPQWu9USi0DMmkaKXSP1rrXXLafNWsWBw8ePKXsiy++4Ne//vUpZbGxsXz00UfdGZoh9h5umqwtvp9/c9kN5wxkzsh+eHmc2vZwN5v4wxWJXPfSDzz7VRa/vmgYSim+3l3M7W+kABAjyb/XGGudnmPrgaPMTogwOJqep93kr7VeT+v9+ADnn2Wfx4HHuxBXjzJ79mxmz+4xtzvYZP+RphE6p7fYA7xbH5lzTlwIV43tz4vf7CP14FHeuv0cvstqmujLw2zinNhgxwYsnEZi/wB8Pd34YsdhSf4OIHfECIfaV1JJhL/XKX377fnTlSO5c9ogNuWUsfNQBRv2HWHKkFD2/PEiEvtLV1pv4eXedO/Gqu2FHK2qa38HYRNJ/sKh9pdUnTIuvyO83M0snBQDwOrMIvYWVXLuoJAeeQOcaNuNEwdS12DhQxnyaXeS/IXDaK3ZX1Jpc/IHiAjwol+AF29uyAXgvMGhdo5OuIJhEX4Mj/Dj693tT/ctbCPJXzhMaVUdx2oaiAv17dT+YwYGUVXXyOjoQEZKd0+vNWVIKCm5R6muazA6lB5Fkr9wmP0lZ07HYIuToz3umjZIunx6salD+1LXaGHa39Yx/W9reWr1HqND6hEk+QuH2XP4GND01b0zrhsfzTPXJXFhKzd8id5jfEwwSkHJ8Vo08NqGXJn0zQ5ktQThMJmFxwjydifC36tT+/t6ujFXZurs9bzczXz2syn4e7mzp+g4t762hW/3Hmn1LnDRcdLyFw6TeegY8f38pctGdNnwCH/6BfbhvMGhBHq7syrDJWeMcSqS/IVDNDRa2H34OPGR/u1vLEQHuZtNXJQQwZeZRdTUS9dPV0jyFw6Rc6SK2gbLKdM6CGEPc0b1o6qukbUy/LNLJPkLh9heUAFAfKQM0RT2NTEumBAfDz5K7T1LnjqCJH/hEJv2lxHQx50hYZ0b4y/E2biZTdxwzgBWZxaxIk0+ADpLkr9wiB9ySpkQG4zJJBd7hf3dd/4Qxg0M4ncrd8rNX50kyV/Y3aHyExworWZiXIjRoYgeyt1s4uGLh1NeXc8HW/NptDjlYoBOTZK/sLvNOWUAMv2ycKhxA4MYHR3Ib1fsZMpfvpbF3m0kyV/YXerBo/h4mBkhwzyFAymleHxuIpeOiuRQRQ3peeVGh+RSJPkLu0vLK2dkVABm6e8XDpbYP4DH5yaiFHyfXWp0OC5Fkr+wq9qGRjILjzE6OtDoUEQvEejtwcj+AXyffcToUFyKJH9hV5mHjlHfqBkjyV90o8mDQ9l28Ch7rGtGi/ZJ8hd2U99o4c2NBwBIig4yOBrRm9xybgwhvh7c9voWKmtl6GdHSPIXdvOf73L4KLWAe2cMJiKgczN5CtEZEQFePLdgLAXlJ/hwqyz52BGS/IXdbDt4lEF9fXhw9jCjQxG90ITYYEZHB/LGxlwsMu6/XZL8hd3sLTrO8AgZ3imMc9vkGPaXVPGBtP7bJclf2EV1XQMHy6oZGt65VbuEsIfLRvVjQkwwv1+VyU/eTGH1zsNoLd8CWtNu8ldKvaqUKlZK7WhRtkQpVaCUSrP+u6TFaw8rpbKVUnuUUrMdFbhwLtnFlWgNwyJkIjdhHJNJ8eS80YwdGMTOggoWvbWVtzcdNDosp9SRlv/rwEWtlD+ttU6y/vsUQCkVD8wHEqz7vKCUMtsrWOG8Tg6xGybdPsJgA0K8efO2CXz7qxmcGxfC377YQ1lVndFhOZ12k7/W+lugrIPHuwJYqrWu1VrnANnAhC7EJ1zEnsPH8XQzMSDY2+hQhACapn5ecnkCFSfqeWfTAaPDcTpd6fO/VymVYe0WOjmouz+Q12KbfGvZGZRSi5RSKUqplJKSki6EIZxBRn4F8f38ZUoH4VSGRfiRFB3Imswio0NxOp1N/v8CBgFJQCHwd2t5a3/5rV5t0Vq/pLVO1lon9+3bt5NhCGfQ0Ghhe0EFSXJXr3BCFyaEk55fQWHFCaNDcSqdSv5a6yKtdaPW2gK8zI9dO/lAdItNo4BDXQtROLs9Rcc5Ud8oyV84pQvjwwF47utsGmTa52adSv5KqcgWT68ETo4EWgnMV0p5KqVigSHA5q6FKJxdmnUqXUn+whkN6uvLwkkxvLPpIE+t2Wt0OE7Drb0NlFLvAtOBUKVUPvA7YLpSKommLp1c4KcAWuudSqllQCbQANyjtW50SOTCaWTkVRDk7S4Xe4VTUkqx5PIEio/X8Pamg9w3cwh9PMys21NMQ6NmlvWbQW/TbvLXWi9opfiVNrZ/HHi8K0EJ17L/SCVDwv1QSi72Cud16+RYPt1+mL9+sZuk6EAefD8dd7OJ7389kyAfD6PD63btJn8h2pNzpJrzh4cZHYYQbUoeGMRFCRG89n0uABH+Xhw+VsOCl3/A38udGyYO4IqkVgcn9kiS/EWXVNY2cKSyloGh0uUjnJtSihdvGsf+kkoOH6shsX8Av1m+nbW7iwn39+KB99IYEx1EdHAf9hZVMjTct0d/m5XkL7ok90gVALEhPgZHIkTHxPX1Ja5v0zQkT1+XRKNFU15dz+S/fM1/Nx0gLtSHxcu38+CFQ7l35hCDo3UcmdhNdEluaVPyHyjJX7ggd7MJL3czEQFeXBgfzntb8lieWgDAk6v3sj7rCFprPtteyPGaeoOjtS9J/qJLDpRWAxAj3T7Cxd0xJY6KE/Vszinj5nMH0i/Ai7+t3sPmnDLuensbi5dvNzpEu5LkL7ok50gVYX6eeHtID6JwbeMGBjF1aNNsA5eMjOT+84eQnlfOrz7MAOB/GYU9apoISf6iS/aXVBLXV7p8RM/w2znx/GRKLMkDg5iXHM34mCAOlFZzzbgoYkN9eP7rrB6zPoAkf9FpWmvrqAhZwEX0DIPDfHnk0njczCbMJsXT1yVx/vAw7pw2iNvOiyU9v4ItuUeNDtMuJPmLTjtUUUNlbYMkf9FjRQV588rC8QwO8+WasVEEebvz0rf7jQ7LLiT5i07bW3RyARdJ/qLn6+Nh5qaJA/lqdxGf7yikvNq1F4iR5C86ba919a6hYZL8Re9w07kxuJtN3Pnfbcx66hu25Datc7UsJY9ffZBObYPrTGUmyV902p6i44T7exLg7W50KEJ0i75+nnx45yRevHEs3h5uPPR+OpW1DTz+v10sS8ln8YeuMxxUkr/oFK012w4cJT5S1uwVvcvIqAAuSozkwdnDyC2t5r53tlFxop6pQ/vyUWqByywaI8lfdMr2ggpyS6u5KDHC6FCEMMTFiRFE+Huxdk8J5w0O5XeXxQOweqdr3Asgd+aITlmRdgh3s+KihMj2NxaiB3I3m/jPLckUH69h6pC+uJlNDAnz5fMdh7llUozR4bVLWv7CZo0WzaqMQ0wfFib9/aJXS+wfwMzh4biZm1LppaMi+SGnlB/2lxocWfsk+Qubbc4po+hYLZeP7md0KEIYY8t/IOP9M4p/MiWOAcHe/HJZOkernHsoqCR/YbOV6Yfw9jAza0TvXP5OCNY/A8vvgI/uhLqq5mIfTzeenT+GkuO13P32NhotzjsVhCR/YROtNWsyizh/RDh9PMxGhyOEMSwN4BcJ6UvhzbnQ8GMrPyk6kN9eFs/G/aVscuLuH0n+wiYF5Sc4UlnLhNhgo0MRwjiWRhg6G655BfI3w8r7oKai+eWrx0bRx93M/7YXGhhk2yT5C5uk5zX9go+OCjA4EiEMZGkAkxskXg3Tfg0Z78FLM5q7gPp4mJk5PIwvdh6modFicLCtk+QvbJKRX46H2cTwCLm5S/RiuhGUtdtzxm/gxg+hbB+s/j+wTvl82eh+HKms45OMQwYGenaS/IVN0vLKGdHPHw83+dURvZilsanlf9Lg82HiPZDyKiy7GWoquDA+nMT+/vz18z2cqHO+OX/kL1h0WFlVHen55YyJDjQ6FCGMZWkE02npc/bjcMEfYPf/4N/TMB1O5+GLR1BYUcPqzMPGxNkGSf6iw/79zT7qGizcOHGA0aEIYayTff4tKQWT74dbP4WGWnjlQs5120tfP0++2OmCyV8p9apSqlgptaNFWbBSao1SKsv6M6jFaw8rpbKVUnuUUrMdFbjoXmv3FPPahlyuSOrPYJnCWfR2Lfv8TzdgIvz0WwiIwvTeDdzXP4t1e0qoqXeurp+OtPxfBy46rWwx8JXWegjwlfU5Sql4YD6QYN3nBaXO9j8kXMGuwmP8/pNM7ngjhaHhvvx2TrzRIQlhLK1BW85s+bfk2xdueB98w7k5dzHnN3zH2t3F3RdjB7Sb/LXW3wJlpxVfAbxhffwGMLdF+VKtda3WOgfIBibYJ1TR3XYVHmPOc+t59fsc5o2L4t2fTCTIx8PosIQwlsXagje1064NGQR3fofuP57HPV5jxffpjo/NBp3t8w/XWhcCWH+GWcv7A3kttsu3lp1BKbVIKZWilEopKSnpZBjCkVbvLMKiNd8+NIMnrh6Fn5dM4iYEloamn+0lfwCzO+qCJfhTRd3BLWQXH3dsbDaw9wVf1UpZq5NbaK1f0lona62T+/bta+cwhD2s3VPM6KhABoR4Gx2KEM5DW1v+He3R9my6J8bLpHlz4wEHBWW7zib/IqVUJID158nOrHwgusV2UYBz3uEg2lRaWUt6fjkzhoW1v7EQvUlzy7+Dy6FYt5sw0J/l2wqorG1wUGC26WzyXwncYn18C7CiRfl8pZSnUioWGAJs7lqIwgibc8rQGqYMDTU6FCGcS0f7/E+yJv9pQ4KorG1gRVqBgwKzTUeGer4LbASGKaXylVK3A08AFyilsoALrM/RWu8ElgGZwOfAPVpr5xrfJDokLa9pGoeEfjKNgxCnaE7+HW35N31IxAR5MjjMlxVpztEZ0m70WusFZ3np/LNs/zjweFeCEsZLzSsnvp8/nm4yUleIUzT3+Xew48T6IaEsDVw2qh/PfLWXwooTRAb0cVCAHQzL0HcXTqmh0cL2/AqSZBoHIc5ka5+/2b15v8uT+qE1fJJufOtfkr84w96iSk7UN0ryF6I1nezzx9JAbKgP4wYGsXRLHlobu8qXJH9xht2HjwGQ2F/6+4U4QydH+5z80FgwYQD7S6rYnHP6vbPdS5K/OENuaTVKQXSwjO8X4gzaujhLR8f5n/yGYP3QuHRkJIHe7vzhf5mGzvcjyV+c4WBpFf0C+sjFXiFa04VuH2ha5etv14xmR8ExHvsk0wEBdjAsw97ZTpxxkQRXl1tazUC5q1eI1tkyvQOckfwBLogP567pg3h380E+2Jpv5wA7GJYh72onOUeqmPCnL3nis93yIWBHB0qrGBjiY3QYQjgnbes4/1P7/E/65QVDOTcuhIz8cvvFZoMORu+c3EyKmcPD+Pe3+6iqbeAPcxONDsnlVZyo52h1PTHS8heidSdb8B3t8z95P4Dl1Gkd3MwmXrt1PF7uxnSvunTLPzrYm2fnj2HhpBj+u+kAqQePGh2SyztYWg0g3T5CnI3FesG3oy1/pZq2tZw5p49RiR9cPPmf9MsLhxHYx51/f7Pf6FBcXm5pFYB0+whxNs19/jakz7MkfyP1iOTv6+nGdeMHsDrzMAXlJ4wOx6UdLJOWvxBtsrXP/+S2Fue6Ltkjkj/AjRMHoIEPUoy5ct5T5B6pIszPE28Pl74cJITj2NrnD00jg6Tl7xhRQd6MHRDEl7uKjA7FpR2QYZ5CtM3WWT1PbivJ33FmDg9je0EFRcdqjA7FZR0ok2GeQrTJ1pu8oCn5N9Y7Jp5O6lHJf9aIcABW7zxscCSuqbqugaJjtTLMU4i26E4mf+nzd5yh4b6MigrgydV7ybNeuBQdd/Ji7wBp+Qtxdp3q85duH4dSSvH8grFYtObRFTuMDsflHLCO8ZeWvxBtkD5/5zQgxJt7Zgxm3Z4Sw6dMdTV7Dx8HICZUWv5CnJWtc/uAJP/ucvO5Awnz82T+Sxv55bJ0mfengzbllDE8wg9/L3ejQxHCeWkb7/A9ua0kf8fz9nDjw7smcdvkWJan5rPorRTDV81xdnUNFlIOlDExLsToUIRwbs19/rbc4WuWC77dJTrYm/+bE8/v5sTzXdYRPtshI4DakpFfTk29RZK/EO2RPn/XcNO5MQyP8OMPqzKpOOFc42ydyQ/7SwE4JzbY4EiEcHLS5+8azCbFX64eRfHxWh5budPocJzWD/ub+vuDfDyMDkUI59bpuX0k+Xe70dGB3DVtEMtTC9gm0z6foa7BwtYDR6XLR4iOONntY/PcPj2oz18plauU2q6USlNKpVjLgpVSa5RSWdafQfYJtWvumj6IUF9Pnvh0t1z8Pc32gnJO1DcyMU66fIRoV2end+iBLf8ZWuskrXWy9fli4Cut9RDgK+tzw/l4unHfzMFszi1jS660/lvakN3U3z8hVlr+QrRL+vzP6grgDevjN4C5DniPTrk2OZpgHw9e/Gaf0aE4Da01H6cVMG5gEMHS3y9E+6TPHwANrFZKbVVKLbKWhWutCwGsP8Na21EptUgplaKUSikpKeliGB3Tx8PM7efF8vXuYpZvk3n/AVLzytlXUsW1yVFGhyKEa+gh8/l3dcWOyVrrQ0qpMGCNUmp3R3fUWr8EvASQnJzcbZ3wi6bG8V1WCYuXbycm1IcQH49ePYXxGxty6eNu5tJR/YwORQjXYOsavie3dbLk36WWv9b6kPVnMfARMAEoUkpFAlh/Fnc1SHtyN5t44YZx9PX15KoXNjDtb+t4b8tBo8MyRFpeOSvSDrFwcgy+nrJylxAd0pk1fM3uPSf5K6V8lFJ+Jx8DFwI7gJXALdbNbgFWdDVIewv28eDVheO5akx/xscE8ejHO3lzYy4WS+8aBfT31XsI9fXknhmDjQ5FCNehG21r9YNTtvy70twLBz5SSp08zjta68+VUluAZUqp24GDwLyuh2l/wyL8eOq6JMqr67j3nVR+u2InNfWNLJo6yOjQukV28XG+yzrCQ7OHSatfCFtYGmzr7wenHOff6b96rfV+YHQr5aXA+V0JqjsFenvw1u0T+MmbW3lqzV5mDAtjSLif0WE5RE19I6kHy+nr58kzX2bhYTZx3fhoo8MSwrVYpOXfYyil+MPcBOb8Yz3z/r2Rl29OZnRUIB5uPecG6BN1jVzyj+/IOVLVXHbfzMGE+noaGJUQLsjSaNsYf5Dk78wiA/qw/O5J3PraFq5/+Qe0hnPignnh+nEEeLv+/Pavfp9DzpEq/nr1KI5W19E/qA9zZISPELbTkvx7nIEhPnx41yT++sUeGhotfJxWwP1LU3n91vFYr224pOM19by4bh+zRoRzrXTzCNE1nerzd74F3CX5nybIx4M/XzUSgOGR/vxhVSavrM/h2vHRLrvC1fsp+RyvbeD+82VUjxBd1qk+f+e7yavndGo7wC3nDmR0VAB//N8uZv39G5dcE7i+0cIbG3MZNzCIUVGBRocjhOvrIX3+kvzb4GY28f6dk3jnjnPw8XRjwcs/8Or6HKPDsskLa/dxoLSae2b0jiGsQjhcV/r8nWhGYUn+7fBwMzFpcCgr753MrBFh/H5VJv/5br9L3BCWe6SK577OYm5SP2YODzc6HCF6hs72+cOPi787Aenz7yA/L3f+ef1Y7vzvVv74v12sTD/EyzcnE+7vhcWiMZkUWmuOnWjAv4+bYReID5ZW8/nOQtxMJjbsO4K72cRvLh1hSCxC9Eid7fOHpg8OW781OIgkfxu4mU38+6ZkVqQV8OjHO5jz3HpiQrzZdrCcQX19KKyo4XhNA2MHBLJo6iCmDg3F26N7/ou/2HmYX3+YQXn1qesU3zV9EGF+Xt0SgxC9QmcS+MkPi8Z6cHOOe2sk+dvIbFJcNTaKIWF+PPPlXgrKT3D9hAHkH61mQmwwIT6eLE/N587/bgXg3hmDeXD2MIfGVFhxgl99kEFkgBc/nTqIK5L6oRRsyT3KhfHS3SOEXWlL5+7wBae66CvJv5NGRgXwysLxrb5238zBrNtTwsdpBTy/NptQXw8WTo51SBwF5Se4+ZVN1Dda+NeN44gN/XF66stH93HIewrRq1kaQNl4udRkHSbuRGP9Jfk7gJvZxKz4cKYP60ttg4Uln2RS02Dhp1Pj7HIt4ERdI3/8XyYb95VyqOIEJqV4beH4UxK/EMJButrn7yRktI8DuZlN/PP6scwZFckTn+3m/qVpVNf9ePK35Jbx8/fS+MdXWaeUt+eB91J5Z/NBYkJ9uHJMFF88MJVz4mT9XSG6RVf6/J0o+UvL38E83Ew8t2AMIyL9eXL1Hr7YcZhhEX4kxwSxbEseJpOisraBj9MKeG7BGBL6BbR5vB0FFXyxs4gHZg3hgVlDu6kWQohmnZ3PH5wq+UvLvxsopbhnxmCW/mQit06Owd2sWLo5j/5BffjyF9N4+/ZzqKpt4Mp/bmBVxqGzHkdrzZOr9+Dn5cZt5znmGoIQoh2Wxs6P83ei5C8t/250TlxIq90z4f5efPazqfz0rRTufSeVp1bvZWCIN3fPGMzQMD98PM2YlOKfa7NZt6eE386Jd9l5hoRweZZG24drNvf5ywVfcZpgHw/evO0c3vohl5Tco2TkV3Ddvzdi0RDo7U4fdzOFFTVcMjKCWyfHGB2uEL2XpQFM3rbtIy1/0ZY+HmYWTR3EoqlQcaKeF9Zl4+PhRl5ZNbUNFqYP68vcpP4uPb20EC6vh/T5S/J3UgF93Hn4YpmWQQin05W5fZwo+csFXyGEsIXF0oWhns7T5y/JXwghbNGpKZ3lJi8hhHBtloYe0ecvyV8IIWzRpXH+9W1v140k+QshhC06NbeP9PkLIYRr041gsjF1mntRt49S6iKl1B6lVLZSarGj3kcIIbqV9PmfnVLKDPwTuBiIBxYopeId8V5CCNGtZG6fNk0AsrXW+wGUUkuBK4BMu75L0U744Da7HlIIIdp0oqzz4/w//w2se+LU1wbPgtmP2yc2Gzgq+fcH8lo8zwfOabmBUmoRsAhgwIABnXsXNy/o69glEoUQ4hR9h8PIebbtExQDybdBdemZr/n3s0tYtnJU8m9t8hl9yhOtXwJeAkhOTtatbN++kEFw7Zud2lUIIbqN2R3mPG10FKdw1AXffCC6xfMo4OwT1QshhOhWjkr+W4AhSqlYpZQHMB9Y6aD3EkIIYSOHdPtorRuUUvcCXwBm4FWt9U5HvJcQQgjbOWxKZ631p8Cnjjq+EEKIzpM7fIUQoheS5C+EEL2QJH8hhOiFJPkLIUQvpLTu3P1Vdg1CqRLgQBcOEQocsVM4zkLq5BqkTq6hp9bJR2vdtzM7O0Xy7yqlVIrWOtnoOOxJ6uQapE6uQep0Jun2EUKIXkiSvxBC9EI9Jfm/ZHQADiB1cg1SJ9cgdTpNj+jzF0IIYZue0vIXQghhA0n+QgjRC7l08u8pi8QrpXKVUtuVUmlKqRRrWbBSao1SKsv6M8joONuilHpVKVWslNrRouysdVBKPWw9b3uUUrONibptZ6nTEqVUgfVcpSmlLmnxmivUKVoptVYptUsptVMp9TNrucueqzbq5LLnSinlpZTarJRKt9bpMWu5/c6T1tol/9E0VfQ+IA7wANKBeKPj6mRdcoHQ08r+Ciy2Pl4M/MXoONupw1RgLLCjvToA8dbz5QnEWs+j2eg6dLBOS4AHW9nWVeoUCYy1PvYD9lpjd9lz1UadXPZc0bQaoq/1sTuwCZhoz/Pkyi3/5kXitdZ1wMlF4nuKK4A3rI/fAOYaF0r7tNbfAmWnFZ+tDlcAS7XWtVrrHCCbpvPpVM5Sp7NxlToVaq23WR8fB3bRtOa2y56rNup0Nq5QJ621rrQ+dbf+09jxPLly8m9tkfi2Trgz08BqpdRW68L2AOFa60Jo+uUGwgyLrvPOVgdXP3f3KqUyrN1CJ792u1ydlFIxwBiaWpU94lydVidw4XOllDIrpdKAYmCN1tqu58mVk3+7i8S7kMla67HAxcA9SqmpRgfkYK587v4FDAKSgELg79Zyl6qTUsoX+BB4QGt9rK1NWylzynq1UieXPlda60atdRJNa6BPUEoltrG5zXVy5eTfYxaJ11ofsv4sBj6i6etakVIqEsD6s9i4CDvtbHVw2XOntS6y/lFagJf58au1y9RJKeVOU5J8W2u93Frs0ueqtTr1hHMFoLUuB9YBF2HH8+TKyb9HLBKvlPJRSvmdfAxcCOygqS63WDe7BVhhTIRdcrY6rATmK6U8lVKxwBBgswHx2ezkH57VlTSdK3CROimlFPAKsEtr/VSLl1z2XJ2tTq58rpRSfZVSgdbHfYBZwG7seZ6MvqrdxSvil9B0ZX8f8IjR8XSyDnE0XaVPB3aerAcQAnwFZFl/Bhsdazv1eJemr9b1NLVCbm+rDsAj1vO2B7jY6PhtqNNbwHYgw/oHF+lidTqPpu6ADCDN+u8SVz5XbdTJZc8VMApItca+A/ittdxu50mmdxBCiF7Ilbt9hBBCdJIkfyGE6IUk+QshRC8kyV8IIXohSf5CCNELSfIXQoheSJK/EEL0Qv8PIcsIG6W1O1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 0\n",
    "\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len]\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len]\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len]\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd31697cfd0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 1\n",
    "\n",
    "position = df['Position'].iloc[288:288*2].tolist()\n",
    "input = df['Input288'].iloc[288:288*2].tolist()\n",
    "complement = df['Complementation'].iloc[288:288*2].tolist()\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd34dd8fcc0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 2\n",
    "\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd3168c0080>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S4Wwe3fL3hJKUsEx7p+la+fkY8PM1oFC0dXRavL9ca9f4IqeCWSnRpmkaLKGNWNzV85yaP8Akc/I/cKrW8iCFS3hs8g8MDKSqSuYG8RbdLX8fAwal8Pc1WFz20VpTVVVlUXdSYZu8igbe23uKvIpGLkm3ogVuTVfPrg+JHv9mo0L8iQ8P4HiZTPPgaTy27JOUlERxcTEVFTIzoDeoa26nvqUD37oglIKqhlY6jJqmcssSemBgoAz6cpKK+laWr95BRb1poJ9FN3q7WVP26ar5n/2hPzwyiNK6ZiveU7iCxyZ/Pz8/UlJS3B2GsNAv1u1ja84ZdvzXdAB+++FhXtlZxOFHlsnoTjd76L2D1DW3My89hurGNsbEhVq+s1Vln/Nb/gCJEYEcLe19bIhwH49N/sK7lNa2kBDxdSs/JTaElnYjJXUt3evCCtc7UFzL+kOl3LdkLD9dko7RqPufoK2nriRuxw1fgITwIDYfq0BrK95bOJ3H1vyFdymtayGxZ/I3dyUsqGh0V0gCeGZzLuGBvnxvbjKAdd/CupO/lTV/zm75J0QE0NTWSX2rTOnhSST5C4corW05a6Kw1BhTaaGgUm70uUtHp5Etxyu4eupwwgNtmUG1K4lb28//nJZ/hOmbX2ltiw0xCGeR5C/sVt/STkNrx1kt//jwAIL8fMivlJa/uxw8XUdjWyezU6NtO4DNZZ/za/4AJZL8PYrFyV8p5aOU2quU+tD8PEoptUEplWP+OazHax9USuUqpY4ppZY6I3DhObpadD1r/kopUmJCukeUCtfbmW+aUO3C1CjbDtDVgre46tNHy9/8jbC0Vnr8eBJrWv4/BY70eL4K2Ki1Tgc2mp+jlJoILAcygGXAM0opC1aDEN6qtM6U/BMjzr6xmxIryd+ddhZUkxobQlyYreMnrGz591Hzj+9O/q0Iz2HRVVVKJQFXAv/osflaoGvaxjXAdT22v661btVaFwC5gHXzHQuv0vV1PuGcxUFSY0I4Wd1Em8zx43KdRs1XBdVcmGJjyQd6tODtq/n7+xqICfWXvv4extKP9CeBXwI9r2q81roEwPwzzrx9BHCyx+uKzdvEINVV9okLP3sin5SYEIwaTlRL69/VDp+uo761g9m2lnzAYTV/MH0rPFUjNX9PMuBVVUpdBZRrrXdbeMzemgnn/TUope5SSmUppbJkFK93K61rITrEn0C/s6t7XZN67So4446whrSdBeZ6vyNa/lbP6nn+N73hkYGcOtNkeyzC4Sz5SL8YuEYpVQi8DixSSr0ClCmlEgHMP8vNry8GRvbYPwk4fe5BtdartdYztNYzYmOtGW4uPE15XQuxYedP3zkmLpQRkUFsPlbey17CmXbkV5McHXzWTXjr2djVsxcjIoM5VdMsc3V5kAGTv9b6Qa11ktY6GdON3M+01t8BPgBuNb/sVqBrqagPgOVKqQClVAqQDuxyeOTCY1TUtxLXy2LgSinmj4vly9xKqfu70JnGNrblVXJRmp1TKFtb9qH3Eb4AI4YF0dJupLqxzb6YhMPY08//MeBSpVQOcKn5OVrrQ8CbwGFgPfAjrbXlc/sKr1Ne30pcLy1/gIXj4mhs6ySrsNrFUQ1NRqPm2S15NLd38r2Lk+07mNVln75r/l1TfJyWur/HsGpuH631ZmCz+XEVsLiP1z0KPGpnbMILGI2ayobWXss+AHPSovH3MbDpWLks5uFktU3tXPu3rRRWNXHl5ETGxofZeUTHzO0DkDTMlPxP1TQxOSnCzriEI8gIX2GXmuZ22js1saG9J/+QAF9mpUSx6Zjc1He2pzbmcKK6iUeuyeCPN0yx/4Da1pp/3y3/4jPS3dNTSPIXdumaI/7cbp49LRgXS255AyerpbeHs+wuOsPL2wu5aeYobp2TTEiAAybsdWDNPzLYj2B/H07VSPL3FJL8hV26kn9fLX+AxRPiUco0w6RwvLqWdn7yzz0kRgay6vLxjjuwzV09z2/5K6UYERnEKWn5ewxJ/sIu5fVdA7z67lKYEhPCXZek8tquk3yR03v55+CpWj49XOaUGAe757bkc7q2hb8sn0ZEkC2zd/bFym6Z/dT8wdTjR1r+nkOSv7BLd8u/jxu+XX526VgSwgNZvSX/vN91GjU/+uce7nw5iwfe2t+9HrAY2JnGNv7xRQFXTklk2qhhA+9gDatH+PZd8wdT3V+Sv+eQ5C/sUlHfSpCfDyH+/c/dF+Drwy0XjuKLnEryK86e4/+TQ6UUVTWxcFwsb2Sd5N43sp0Y8eBy8HQtze2dfPvCUY4/uLVln35q/mBq+dc0tdMoi7p4BEn+wi7l9a3EhQdYtDzf8lkj8fNRvPhlIeV1LbS0d7LxSBn//d5BRkcH849bZ3LPojF8tL+Eg6dqXRC99zthvonetXKaY9nY8u9jFG9Xjx9p/XsGSf7CLhX1rf3e7O0pLiyQ66cn8UbWSRY8vpmlT27hB2t3kxARyD++OwMfg+KOuakE+Br4564TTo58cDhR3YS/j4F4m6dt7ofVs3r2PcgLevT1l5u+HkGSv7BLRT8DvHrzg/lpdHQaiQrx50xjG+MSwnj9rtmkmwckRQT7cdWU4by/9xQNUh4Y0MnqJpKigqxbm9dS1q7hO0DNf3hXX39p+XsEB3QGFkNZeV0LF6dZPnNkSkwI61ZexKioEPx9DAT6GwjwPft+wS0XjuLtPcW8n32Kb1842tEhDyonqpsYFRXspKM7rp8/mL75+RqUtPw9hCR/YbOW9k7qWjqsavkDXDC6/znmp4+KZHxCGC9sLWBYsD+XT0qw6J7CUHSiqonpju7l08XBZR8fgyIxMlBq/h5Cyj7CZpUNlnXztJZSijvmppBX0cjdr+7h5e1FDj3+YFHb1E5dS4fzWv42L+bSd1fdpMhgGentIST5C5uVd03t4ISbjTfOGMnBR5aycFwsj/77CMWyEMh5iswrpI10WvK3tqsnpg+KfpL/+MQwjpXW0yFjOdxOkr+wmaUDvGwVGuDL/143ifZOI2/tLnbKe3izTUdNo6WnOHuWTItr/mAqEfU9MnjyiAia2zvJq5ClPd1Nkr+wmbOTP0DSsGDmpEXzzp5TsgpUD1pr3ss+xezUKBIjgpz0JlbW/GHAln/XB9X+4hrb4xIOIclf2Ky8vhWlIDrE36nvc/30JE5UN7HnhKwFDKZ5kK55+ksKKhv5xrQRznsja7t6dr22nw/plJhQQvx9ZBCfB5DkL2xWUd9KdIg/vj7O/TNaMjEeX4Niw2FZCxjg6c9yKaxs5PrpSVw1ZbgT38mW5N9/y9/HoMgYEcG+Ykn+7ibJX9isor6VGAtH99ojPNCPWSlRbDwis36eqmnmk8OlfOei0fzpW1MdM29/X2wp+wxQ8weYNiqSQ6draW6T1V3dSZK/sFlFfUu/Uzk70uIJ8eSUN3Ciauj2+unoNPLf7x7AoJRzJnI7l9WLuZhfO8C9mdkp0bR3avaelDKeO0nyFzazZl4fey0eHwfAxqNDt/W/dkcRm45V8D/XTiJpmLNG9fZgU1fP/mv+ABckD0Mp2FVQbUdwwl6S/IVNtNZWz+tjj+SYENJiQ/js6NCt+3+ZW0VqbAi3uKLVD3xdvnFczR9MZbyJieGS/N1Mkr+wSU2TaeH2OBclfzCVfnbkV1Hf0u6y9/QUWmuyT9aQOTLSlW9q+mlV2Wfgmj/AjNHD2HeyRrrvupEkf2GTCidN7dCfxePjaO/UbD7W+1KQg9mpmmYqG1qZ5tLkb0PZBzVgyx9gbEIYjW2dnK5tsS02YTdJ/sImrhjgda4ZyVEkRgQOydG+e0/UAJA50kmTuPXKOTd8AdLjTFN455TV2xKYcABJ/sIm3Qu3uzD5+xgUN16QxJacCk4PsZkhD5yqxd/XwPjEMNe9qU0jfC1r+afHhQKQU9YwwCuFs0jyFzZxR8sfTBO+AUNups+88gZSY0Lwc/KAurN03++1suVvQc1/WIg/MaEB5JRLy99dJPkLm5TXtRLoZyDUmYOMejEyKpirpgxn7fZCzjS2ufS93amgspHUWGes09sPJ9b8wdT6zymXlr+7SPIXNqloaCUuLNAti6zcs2gMjW2dvJl10uXv7Q7tnUZOVDc5aZH2/tja1dOyHjxj40PJLWuQHj9uIslf2KSi3nV9/M+VHh/GyKggDp6uc8v7u1rxmWY6jJqUmFDXvrHNE7tZ1vIfEx9GfWsHpXXS48cdJPkLm5TXt7r0Zu+5xsWHcax0aCT/gkpTacTlLX9bF3OxoOYPctPX3ST5C5u4s+UPMDY+jPyKRto6Bv+KUPnmhU/SXF3zt6WrJwNP79BlbLyp59Jx6e7pFpL8hdVaOzqpbW532bw+vRmXEEaHUVNQOfhXhDpeVs+wYD8ig527bsJ5bF7MxbLkHxXiT3SIP7ly09ctBkz+SqlApdQupdQ+pdQhpdQj5u1RSqkNSqkc889hPfZ5UCmVq5Q6ppRa6swTEK7X1c0zLty9yR/g6CAv/bS0d7L+YCnz0mNd/+Y21fyxuOYPMEZ6/LiNJS3/VmCR1noqkAksU0rNBlYBG7XW6cBG83OUUhOB5UAGsAx4Rinl44TYhZu4q49/T6kxofgaFIcH6U1fo1Gzcu1uLn3ic+paOrhp5kjXB9Fd83d8P/8uY+PDOF5WLz1+3GDAq6pNuj6a/cz/aeBaYI15+xrgOvPja4HXtdatWusCIBeY5cighXt1J/9Q18zl3xt/XwMzk6PYcKRsUCaOv3+ex/pDpVTUtzI6OpiLUqPdEIUNXT2t6OcPptla61s6qGkaepP1uZtFH+lKKR+lVDZQDmzQWu8E4rXWJQDmn3Hml48AenbALjZvO/eYdymlspRSWRUVQ2+iLm9W7gFlH4ArJieQX9E46MoGDa0dPLMpl6UZ8WxftZh1Ky/CYHD9eApnLebSU4J5MSDp7ul6Fl1VrXWn1joTSAJmKaUm9fPy3v5Kz/tr0Fqv1lrP0FrPiI11Qz1T2KzCvHB7lJMXbh/I0owElIJ/HyhxaxyO9t7eUzS2dfKD+WkMC/EnLsxN37BsXszF8pZ/QoSpASHJ3/Ws6u2jta4BNmOq5ZcppRIBzD+7VtkoBnoWKJOA0/YGKjxHRUMrUcH+rp1nphdx4YFMGh7BzvzBsyiI0ah5eXshExPDXTt9c69sbflbnvzjzS3/Mpna2eUs6e0Tq5SKND8OApYAR4EPgFvNL7sVeN/8+ANguVIqQCmVAqQDuxwct3Cj8jr39vHvKXNkJPuLa+g0Do66/0cHSjhe1sBdl6S6ZeqMs2gba/5W3PDt+lYjLX/Xs+QjPRHYpJTaD3yFqeb/IfAYcKlSKge41PwcrfUh4E3gMLAe+JHWutMZwQv3cOXyjQPJHBlJY1sneRXOr/u3dRjZlluJ0YEfNF03z8G0WtdfNuYwNj6Uq6cOd9h72Mymrp7Wtfz9fQ3EhPpTJsnf5QacklFrvR+Y1sv2KmBxH/s8Cjxqd3TCI1XUtZAW647eJ+fLHBUJQPaJmu4Ro87y6EeHWbO9iHsWp/OzS8fafbz3s0/x09ez+e11k5idGk1Dawc55Q387huT8XHHDd7z2Dq3j3UfjvHhgZRK2cflXDsfr/B6RqOmvL61u5eGu6VEhxAe6MvzWwtIigpiTlqMU95nV0E1a7YXER8ewF825rBkQhxTkiJtPt6pmmYeeHs/SsHv/n2EprZOwgJ88TEolk1KcFzg9nDyCN8uCeGBspyjG8j0DsIqlY2tdBg1CRGekfwNBsW9S8ZypqmNe17bS0v72RVGrTXrD5by2w8Pk2vHwiHPbM4lJjSAf/1kLv4+Bt7Ptq8PwxfHK2hpN/L/bphKS3snwyMCqW/tYE5atNt7UXWzpaunlTV/MN24l7KP60nyF1YpqzXVqOM9pOUPcPvcFJ68KZPKhjbe23vqrN+9svMEK1/ZzT+2FnD5U1/YNI9Mbnk9m49VsGL2aOLCArlkbCz/PlBiV+1/z4kzDAv24/rpI9jyy4V89osF3DxrJD+cn2bzMR3OBV09wdTyr25so7VDbg26kiR/YZWuXhmeUvbpclFaNBMTw/nH1oLupKy15sUvC5iaFMGW+xfS3qn5+FBpv8epaWrj9V0nWH+whD0nzvCvfadZt7sYX4Pi27NHAXDllARKalvYWWBbF1OjUbO76AzTRw1DKUXSsGAC/Xz4/TenMGeMc8pWtrGlq6f1Nf/R0cHA17OXCteQmr+wSnfy95CyTxelFN+/JIX73tjHA2/vp6a5nfjwAPIrGvnTjVMZFR3MpBHhbD5Wzo8WjunzOLe/9BV7TtSctS0m1J+L0qKJMc9iujQjgdiwo/xh/VHe+eEcq0bfbsur5JbndgLwzelJ1p+oK9lc87eu5T/VPJ5h38kaJiSGW7WvsJ20/IVVympb8DGo7kToSa6cPJz48ADW7S5md9EZ/rnzBDNGD+PKKYkALBgbx+6iM9T2MY/ModO17DlRwwPLxvO/12Zw25xk/HwUlQ1tLM34+iZssL8vDywbT/bJGh56/yAt7Z20dxpZuXY3//giv98YP9r/9WjkC1OiHHDWTmRrV08ra/7J0cFEBvuRfbLGqv2EfaTlL6xSWtdCXFiAh3RFPJu/r4HHrp/CkZI6fnCJqXbeM87LMuJ5elMuP3x1N/+34gLCAv3O2v+Nr07i72vg5lkju+fOr21u573sU1w6Mf6s135z2giOl9Wzeks+WYVnmDg8nPWHSvn0SBmXjI3ts9vpjvwq5qXH8Mul45mcFOHI03cCWxdzsa7lr5RialKkJH8Xk5a/sEppbYtH3ew918Jxcdy9YAw+BnXeB9SUpEgev3EqO/Kr+NMnx7u31za3s/5gCa/uPME1U4eftWjKQ1dN5NU7LjzvnA0GxX9dMYGXvjeTM01tvLv3FMsyEggN9OWP64+eF9cXORVc9sTn5FU0ckl6rBckflzW1RNMg/WOl9XT2Nph9b7CNtLyF1YprWthTKyLFxJ3oBsuSGLPiTO8sqMIH4PiwpQoHnh7P2ea2kmNCeHXV0886/VRIf793oRdMC6OHQ8uprKhlagQf5749Dh/35xHSW0ziRFB3a/784bjHDevVXtRmmcMkBuQTbN6Wt/yB9NgPaOG/cW13vP/x8tJy19Ypay2xeNu9lrrZ5eOZVxCGGt3FHHX2t10GDVPLc/k7R/OIfycUpAlDAZFXHggvj4GbpoxCqOGV3ec6P79odO17D1Rwx1zU/jjDVPIGO4lNzVdVPMHyDQPmJPSj+tIy19YrLG1g/rWDo8u+1giJjSAj+6Zx5nGNp7amMNlGfEOGxk8KjqYyyaa7i3UNrdz/7Jx/M+/DhPk58M9i9KJCLb+w8V9bF3MxfrkPyzEn+ToYLJPnrF6X2EbSf7CYl938/S8nj62GBbiz8PXZDj8uH+9ZRqPf3yM574oYO2OIgCevCnTyxI/dkzsZtvgt8yRkWzPr7JpX2E9Sf7CYl1zrnt7y9/ZAnx9+NWVE1k8IZ4vcyuZmBjO5ZMT3R2W9Wwd4Wu0baRu5shI3ss+zemaZoZHBg28g7CLJH9hMU8d3eupZqdGM9sta+86io03fG2o+QPMTTet6Pfu3lP9DsQTjiE3fIXFPHV0r3ASW7p62tDPv8uYuFDmjolh7fYi2jttO4awnCR/YbHS2hbCA30J9pcvjEOCCxZwP9dtc5IprWth87EKm48hLCPJX1isdBB08xRWcNGsnj3NHxdLWKAvnwwwAZ+wnyR/YbGyOs8e3SsczYaunjb28+/i52Ng8fg4Pj1SRoeUfpxKkr+wWGldi9zsHUpsXczFjpY/wGUZCZxpaierSPr8O5Mkf2GRtg4jFfWtJErZZ+iwqexjX80fYG56DAYF2/Okz78zSfIXFjleVo9RQ7qTF0kXnsR1c/v0FB7ox8Th4ewskOTvTJL8hUUOl9QBeM+8NMJ+2vU1/y4XpkSz90SNLO3oRJL8hUUOn64j2N+H5OgQd4ciXMWW6R1snNvnXLNSomjtMLK/uNbuY4neSfIXFjl8uo4JieFWLVkovFx3zd+1ZR+AWcmmVc6yCuWmr7NI8hcDMho1h0vqpOQz5NjQgrdhAffeDAvxZ3R0MPuLa+w+luidJH8xoOziGhpaO5g2KtLdoQhXsnmEr2P6509NimSfzO/vNJL8xYDe33uKAF8DSybED/xiMXjY0tUT2yd2O9fUkZGcrm2h3DynlHAsSf6iXx2dRj7cX8KSCfHnLXguBjv3tvwzR5rWOd4nN32dQpJ/P3x8fMjMzCQjI4OpU6fy5z//GaPRcUPOd+3aRWZmJpmZmUydOpV3333XYcd2lE+PlFHV2MZ100a4OxThajYt4O6Ymj9AxvAI/H0N7JAFXpxCpmfsR1BQENnZ2QCUl5dzyy23UFtbyyOPPOKQ40+aNImsrCx8fX0pKSlh6tSpXH311fj6es5leWlbISMig1g0Ps7doQhXs3klL8c0kAL9fJidGs2mo+U8dNVEhxxTfE1a/haKi4tj9erVPP3002itKSwsZN68eUyfPp3p06ezbds2AFasWMH777/fvd+3v/1tPvjgg16PGRwc3J3oW1paUFbVVp3vQHEtO/KrWXHRaHyki+cQZOPcPg6q+QMsHh9HfmUj+RUNDjumMBnwqiqlRiqlNimljiilDimlfmreHqWU2qCUyjH/HNZjnweVUrlKqWNKqaXOPAFXSk1NxWg0Ul5eTlxcHBs2bGDPnj288cYb3HPPPQDceeedvPjiiwDU1taybds2rrjiij6PuXPnTjIyMpg8eTLPPvusx7T6tdY8tv4Iw4L9uOXCUe4OR7iDTWUfx7X8ge5vnBuPlDvsmMLEko/0DuDnWusJwGzgR0qpicAqYKPWOh3YaH6O+XfLgQxgGfCMUsrHGcG7gzZ/FW5vb+f73/8+kydP5sYbb+Tw4cMAzJ8/n9zcXMrLy3nttde4/vrr+03oF154IYcOHeKrr77i97//PS0tntGzYX9xLV/mVvHjRemEy43eocnmrp6OC2FkVDBTkiJ4e09x97894RgDXlWtdYnWeo/5cT1wBBgBXAusMb9sDXCd+fG1wOta61atdQGQC8xycNxukZ+fj4+PD3FxcTzxxBPEx8ezb98+srKyaGtr637dihUrePXVV3nxxRf53ve+Z9GxJ0yYQEhICAcPHnRW+FbJNvevvmJygnsDEe5jU83fMSN8e7pp5kiOltbLVA8OZlXNXymVDEwDdgLxWusSMH1AAF13BEcAJ3vsVmzedu6x7lJKZSmlsioqPH/JtoqKClauXMmPf/xjlFLU1taSmJiIwWBg7dq1dHZ+PQHVbbfdxpNPPglARkZGn8csKCigo6MDgKKiIo4dO0ZycrIzT8NiR0rqiAz2k/n7hzTXLuDel6unDifQz8Dbe4odetyhzuICs1IqFHgbuFdrXdfPzcnefnHeX4PWejWwGmDGjBke+X2uubmZzMxM2tvb8fX1ZcWKFfzsZz8D4O677+b6669n3bp1LFy4kJCQryc8i4+PZ8KECVx33XX9Hn/r1q089thj+Pn5YTAYeOaZZ4iJiXHmKVnsSEkdExLCPe4mtHAhFy/g3pfwQD/mjonhs6PlPHKNlr9JB7Eo+Sul/DAl/le11u+YN5cppRK11iVKqUSg645MMTCyx+5JwGlHBexKPVvz50pPT2f//v3dz3//+993P25qaiInJ4ebb7653+OvWLGCFStW2B+og3UaNcfK6rll1mh3hyLcyeauno5vyy0YF8enR8rJq2hkTFyow48/FFnS20cBzwNHtNZ/7vGrD4BbzY9vBd7vsX25UipAKZUCpAO7HBeyZ/v0008ZP348P/nJT4iIiHB3ODYprGqkpd3I+ERZuGVo84yaP8CCcbEAbDoqvX4cxZKW/8XACuCAUirbvO2/gMeAN5VSdwAngBsBtNaHlFJvAocx9RT6kdZ6yKzIsGTJEk6cOHHWto8//pgHHnjgrG0pKSkeOaIXIKesHoDxCZL8hzRtxLqSDw5bzOVcScOCmZAYznvZp7hzXoqUfhxgwOSvtd5K338Bi/vY51HgUTviGlSWLl3K0qXeM9zhdI2pu+mIyCA3RyLcSmsrB3iBM2r+Xb594Sj++72D7DlRwwWjhw28g+iXjPAV5ymta8Hf10BUiL+7QxHupI1WzuiJ02r+AN+YNoKwAF9e+LLAKccfaiT5i/OU1raQGBEoX62HPI31ZR/ntfxDAnz57pzRfLS/hIOnpM+/vST5i/OU1rZI/35hW9nHSTX/Lj+Yn0ZksB9Pbcxx2nsMFZL8xXlK6ppJjJDkP+TZUvZx0ALufQkP9OOb05L4/HgFTW0dTnufoUCSvziL0agpq20lIUJu9gobW/5OnoNn8YQ42jqMfJkr8/zbQ5K/OEt1UxttnUZp+QtzEvecmn+XmclRhAb48tnRMqe+z2AnyV+cpbTW1M0zQZK/sKnm7/i5fc7l72tg0fg4PtxXQnVj28A7iF5J8hdnKTEnf2n5C9tr/s5t+QP8ZNEYGts6ePqzXKe/12AlyV+cpfhMEwDDZYCXsKmrp2MXc+lLenwYN14wkrU7CjlZ3eT09xuMJPmLsxRVNREa4Eu0DPASWtswyMu5vX16uu/SsfgYFI9/cswl7zfYSPIXZymqamRUVLAM8BJ2jPB1fssfTPelbr84hfezT3NAFnqxmiR/cZai6iZGRwe7OwzhEWyc28fJN3x7WrkgjWHBfjy2/ogs82glSf6iW6dRc7K6idHRIQO/WAx+ts7q6aKWP5gGff14UTpf5laxJaeye3tbh+ti8FYWr+QlBr+S2mbaO7W0/IWJrTV/W/e10Xdmj+KlbQX8Yt0+xsSGMioqmLf2FHPVlEQeuSaDyGC5f9UbafmLbkVVpl4TkvwFYK752zK3Dy676QsQ4OvDw1dnEB3iT2ldC29kneTiMTH850Apt734FY2tMg1Eb6TlL7oVVjUCSNlHmNnQ1bP79a6tvy+eEM/iCfEYjZozTW1Ehwbw8aFSfrB2N899kc+9S8a6NB5vIC1/0S37RA3Dgv0YLgO8BNgxqycurfv3ZDAookMDAFiakcCi8XG8vL2I6/++jbd3F7slJk8lyV90yyo6wwWjo6SbpzCxt+bvAb4/L5XqxjZ2F53hwXcOsOL5nbyyo8jdYXkEKfsIACobWimobGT5zJHuDkV4DFvn9sFtLf9zzU6NYvWKC0iOCeGe1/ZypKSerbmmXkHLZ47E12fotn8l+QsAsgrPADAjOcrNkQiPYfMC7uDqmn9flFJclpEAwPp7L6GlvZPvvrCL/37vIF/kVPB/K2a4OUL3Gbofe+Is2Sdr8PNRTBoR7u5QhKfQ2vr7vXhWy/9cgX4+vP792fxoYRofHypjd9EZd4fkNpL8BQBHS+tIiw0lwNfH3aEIj2HPDV/PaPn3xmBQ3L1gDFEh/kN6OUhJ/gKAY6X1TEiUVr/owaayj2e3/LuEBPhy+8XJbDlewfGyeneH4xaS/AW1Te2U1LYwLiHM3aEIT2JPV08Pqfn355YLRxPga+D+t/bz8vZCd4fjcpL8BUdL6wAYL8lf9GTrYi5d+3q4qBB/7pyXQk5ZPb9+/xCHT9e5OySXkuQvOFpq+to7PkHKPqInGxdzAY+u+fd0/9LxbH9wMWGBvvxh/VHaOowYjd4Ru70k+Q9xRqPm7T3FDI8IJD48wN3hCE9i09w+njXIyxIRQX78dHE6nx+vYMojHzPtfzdwuqbZ3WE5nST/Ie7NrJPsL67ll8vGy8hecTabRvh6T82/pzvnpfLibTP55vQkmts6+dMnx90dktPJIK8hrLapnT9+fIyZycO4NnO4u8MRHseWxVy6dvX8mv+5Fo6PY+H4OMICfFn9RT6XZcSz1DxAbDCSlv8Q9ui/D1PT1MYj10ySVr84nx78Nf/e/HRJOlOTIvnJP/d2d4YYjCT5D1FrthXyZlYxK+enMXG43OgVvbCpq6f39PbpS7C/Ly/cNpOQAB8e/uDQoF0ecsArq5R6QSlVrpQ62GNblFJqg1Iqx/xzWI/fPaiUylVKHVNKLXVW4MJ2b2ad5DcfHGLJhHh+ftk4d4cjPJU22jCdv3fW/M8VFeLPzy8bx478ajYdK3d3OE5hycf6S8Cyc7atAjZqrdOBjebnKKUmAsuBDPM+zyilZL4AD7HpWDmrt+Tx4DsHmJcew9O3TMPHIOUe0Rc7FnPx4pZ/l5tmjmR4RCDPbSlwdyhOMWDy11pvAarP2XwtsMb8eA1wXY/tr2utW7XWBUAuMMsxoQp7lNe3cNfLWfzu30dJiw3h79+5gEA/+VwW/bBrMRfvbvkD+PkYuO3iZLbnV/G3Tbk0t3W6OySHsrW3T7zWugRAa12ilIozbx8B7OjxumLztvMope4C7gIYNWqUjWEIS72+6yTtnZoXb5vJ9FHDCA2Qjl5iALaM8B0ENf+ebp41ii9yKvl/Hx9j/cFS/v6d6SiliA7x9/rGk6Nv+Pb2l9JrE0BrvVprPUNrPSM2NtbBYYieWjs6eXVnEZeMjWXh+Dgigv3cHZLwCt63jKOjhQX6sfaOC3n+1hnkVzSw8PHNXPzYZ/zXuwfcHZrdbE3+ZUqpRADzz647IsVAz6WgkoDTtocnHOHt3acoq2vlzrkp7g5FeBNbZvV00wLuzrZ4Qjyf/Gw+35k9motSo3lv7ykKKxvdHZZdbE3+HwC3mh/fCrzfY/typVSAUioFSAd22ReisEenUfPM5lwyR0YyLz3G3eEIbzLEa/7nGhEZxG+uzuCpmzPx9THwzOZcd4dkF0u6er4GbAfGKaWKlVJ3AI8BlyqlcoBLzc/RWh8C3gQOA+uBH2mtB9ddEi9ztLSO4jPNrJg9WgZyCevYVfMffMm/S1xYIDfPHMk7e05RfKbJ3eHYzJLePjdrrRO11n5a6ySt9fNa6yqt9WKtdbr5Z3WP1z+qtU7TWo/TWv/HueGLgewqMF2ai9Ki3RyJ8D72jPAdHDX/vvxgfhpKwb2vZ3OguNbd4dhERvgOcjvzq0kaFsTwyCB3hyK8jS1ln693dmgonmZ4ZBD/c+0k8isbuWn1dq9cC1iS/yCmtWZXYTWzUqLcHYrwRvbM6jnIW/5g6ga6/t55xIUFcOear7yuBCTJfxDLq2igurGN2SlS8hG2kBu+A4kLC+TF782iw6hZ8fyu7jKrN5DkP4jtyDf9IUrLX9jEltb7IBvkZYmUmBD+8d0ZtHcaWb56O89v9Y7pICT5D2K7CqqJDw9gdHSwu0MR3mgILeZirwtTo/n43ku4bGIC//vhYf66McfdIQ1Ikv8gpbVmV0E1s1KipYunsI0tyzgOoondrBUS4Mvfvj2db04bwZ82HOfTw2XuDqlfkvwHqZzyBkrrWqTkI+wwNBdzsYePQfH76yczPiGMVe8coLqxzd0h9UmS/yC1Zlsh/r4GLp80eJehE042RBdzsVeArw9P3JRJbXMbD7130GMXg5HkPwidaWzj7T3FfCNzBDGhAe4OR3grm0b4Ds2a/7kmJIZz36Vj+ehACb967yAdnZ73YSjz+g5CT2/Kpa3DyB3zZCI3YQ9bBnkN/ukdLLXykjTqWzr4++Y86prbefKmTGqa2/mffx0mJSaE2y9OcesMu5L8B5kjJXW8vL2QGy5IYmx8mLvDEd7Mllk9h3jNvyeDQfHAsvFEBPnx2H+OUt/SQW55AxX1rbQbjXx8qJTX75pNZLC/e+Jzy7sKp8gtb+CW53Z0rz8qhF1s6urZta/nlTncZeX8NFZdPp7Pj1cQ5O/DupUX8fLts8ivbOQnr+3FaHTPB6W0/AeJ1o5OfvLaXpRSvPmDi4gPD3R3SMLr2THCd4jX/M+1cn4aSybEkxwdjK+P6f/Rb66eyK/ePcjaHUXcOifZ5TFJ8h8kHv/4GEdK6vjHd2cwOjrE3eGIwUAP7QXcHW1MXOhZz2+ZNYqNR8o5Xdvslni8Pvk3t3US5O/da2naa0d+Fc99UcB3Zo9iycR4d4cjBgu7JnaTlv9AlFKsXnFB9zcBV/Pqmv+h07XM++NnbPDwkXTO9vjHxxgeEcivrpjo7lDEYCILuDuduxI/eHnLPz48kBGRQdy1Novk6BAuSY/hm9OTmJIUMSSmNCg+08Q7e06RVXSGR67JGPLfgISj2THCV2r+Hs+rk39MaACv3TWb//s8n8Mldbz21UnWbC/imqnDefzGqfj7evUXm379a99pfvnWfprbO0mPC+VbM0a6OyQx2MjcPoOaVyd/gGB/X+67dCwAtc3tvLC1gKc25rCzoIorJw8np7yeAF8fnlqeSUiA158uAPuLa/j5m/uYkhTB/7txKqOjgjEYBv83HeFiUvMf1AZHNjSLCPLjvkvHkjkqkld3nGDtjkJCA3ypa+ngO8/v5KXbZrl1RJ0j1DS18cNX9hAbFsDq784gKsQ9A0TEUCBz+wxmgyr5d1k4Lo6F4+JoaO3A16DYfKyCe17by/XPbuPJmzKZNCLC3SHabNXbB6iob2Xdyosk8QvnsmeEr9T8Pd7gLYoDoQG+BPr5sGxSAi/dPpP6lnZueHYbh07Xujs0m+wqqGb9oVJ+uiSdqSMj3R2OGOxsWsBd5vbxFoM6+fc0Jy2Gf/1kLpFB/tzxUhbrsk6y72QNnW4aWm2tTqPm0Y8OkxAeyB1zZcI24QL2zOopZR+PN2SSP5gWW/7HrTMID/Ll/rf2c+3fvuTC323kzayTHjvndpeXtxeyr7iWVZePJ9BPunQKV7Clq2fXrp7970kM0pp/fyaNiOA/P72EIyV1FFQ2smZbIb98az/ZJ2v4zdUTCfB1X2Jtae/sTuw1TW28mXWSrMIznK5t5uCpOualx3Bt5nC3xSeGGI3tc/tIy9/jDbnkD6al1iaNiGDSiAiunJzI458c45nNeXxyqIzZqVEMC/ZnVkoUSzMSnD5W4J09xazZVogG9hfXMjwikPAgP/IrG2nrMJIaG0JiRCC/XDaOWy9KHhKD14SHsKXs83XT39HRCAcbksm/J4NB8ctl45mTFsOrO4vYV1xDTWM7a3cUkRITwo8XjmHZpASLxwhoralubCMiyK976LbWmqKqJnwMiqRhQTS0dpBT3sBH+0t4fmsBY+JCCQ3w5YcL0iipaaahtYNLxsbyzekjGJ8Q7szTF6If9vTzl5a/pxvyyb/L3PQY5qbHAGA0aj47Ws7v/3OEn6/bx0PvH2TBuFguSo0mLjyQivpW/H0MJEaappeoaW7nle1FHC+vp6Cikca2Tvx9DMxLj0EDe0+c4UxTOwDx4QE0tnbS0NoBwIrZo3noqomDejSy8FKymMugJsm/FwaDYsnEeBaNjyOr6Azv7i1m87EK/n2gtM99IoL8mDoykhmjoxgZFUzxmSbWHywlJMCXSyfGM33UMNo7jXxVeAY/H9PC6qOig2W1LeG5ZAH3QU2Sfz8MBsWslChmpUShteZkdTNnmtpIiAikrcPIqZpmTtc009TWyTWZwwkPPHv08G+uzjjvmCsuSnZR9ELYSRZwH9Qk+VtIKcWo6GBGRQd3bxsZFdzPHkJ4O1nMZTBzWqFZKbVMKXVMKZWrlFrlrPcRQjiJTWUfqfl7C6ckf6WUD/A34HJgInCzUkpWGhHCm9i1mIskf0/nrLLPLCBXa50PoJR6HbgWOOzQdyk7BG/d7tBDCiHMWmqwubfPhofgi8cdHdHgNGYJLH3U5W/rrOQ/AjjZ43kxcGHPFyil7gLuAhg1apRt7+IbCLHjbNtXCNG/2PEw+Qbr9okcDTPugKZK58Q0GIW7Z9S+s5J/b82Fs74Haq1XA6sBZsyYYdt3xOg0+NbLNu0qhHACH1+46s/ujkJYwFk3fIuBnusKJgGnnfReQgghrOSs5P8VkK6USlFK+QPLgQ+c9F5CCCGs5JSyj9a6Qyn1Y+BjwAd4QWt9yBnvJYQQwnpOG+Sltf438G9nHV8IIYTtZDYxIYQYgiT5CyHEECTJXwghhiBJ/kIIMQQpT1i4XClVARTZcYgYYLANKZRz8g5yTt5hsJ5TiNY61padPSL520splaW1nuHuOBxJzsk7yDl5Bzmn80nZRwghhiBJ/kIIMQQNluS/2t0BOIGck3eQc/IOck7nGBQ1fyGEENYZLC1/IYQQVpDkL4QQQ5BXJ//Bski8UqpQKXVAKZWtlMoyb4tSSm1QSuWYfw5zd5z9UUq9oJQqV0od7LGtz3NQSj1ovm7HlFJL3RN1//o4p4eVUqfM1ypbKXVFj995wzmNVEptUkodUUodUkr91Lzda69VP+fktddKKRWolNqllNpnPqdHzNsdd5201l75H6apovOAVMAf2AdMdHdcNp5LIRBzzrY/AqvMj1cBf3B3nAOcwyXAdODgQOcATDRfrwAgxXwdfdx9Dhae08PAL3p5rbecUyIw3fw4DDhujt1rr1U/5+S11wrTaoih5sd+wE5gtiOvkze3/LsXiddatwFdi8QPFtcCa8yP1wDXuS+UgWmttwDV52zu6xyuBV7XWrdqrQuAXEzX06P0cU598ZZzKtFa7zE/rgeOYFpz22uvVT/n1BdvOCettW4wP/Uz/6dx4HXy5uTf2yLx/V1wT6aBT5RSu80L2wPEa61LwPTHDcS5LTrb9XUO3n7tfqyU2m8uC3V97fa6c1JKJQPTMLUqB8W1OuecwIuvlVLKRymVDZQDG7TWDr1O3pz8B1wk3otcrLWeDlwO/EgpdYm7A3Iyb752fwfSgEygBPiTebtXnZNSKhR4G7hXa13X30t72eaR59XLOXn1tdJad2qtMzGtgT5LKTWpn5dbfU7enPwHzSLxWuvT5p/lwLuYvq6VKaUSAcw/y90Xoc36OgevvXZa6zLzP0oj8Bxff7X2mnNSSvlhSpKvaq3fMW/26mvV2zkNhmsFoLWuATYDy3DgdfLm5D8oFolXSoUopcK6HgOXAQcxncut5pfdCrzvngjt0tc5fAAsV0oFKKVSgHRglxvis1rXPzyzb2C6VuAl56SUUsDzwBGt9Z97/Mprr1Vf5+TN10opFauUijQ/DgKWAEdx5HVy911tO++IX4Hpzn4e8Ct3x2PjOaRiuku/DzjUdR5ANLARyDH/jHJ3rAOcx2uYvlq3Y2qF3NHfOQC/Ml+3Y8Dl7o7finNaCxwA9pv/wSV62TnNxVQO2A9km/+7wpuvVT/n5LXXCpgC7DXHfhD4tXm7w66TTO8ghBBDkDeXfYQQQthIkr8QQgxBkvyFEGIIkuQvhBBDkCR/IYQYgiT5CyHEECTJXwghhqD/D6NsRlzkQL4aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 3\n",
    "\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd306b31f28>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 4\n",
    "\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd316c592b0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 5\n",
    "\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd316d91be0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_num = 200\n",
    "\n",
    "position = df['Position'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "input = df['Input288'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "complement = df['Complementation'].iloc[day_num*seq_len:(day_num+1)*seq_len].tolist()\n",
    "\n",
    "for index, pos in enumerate(position):\n",
    "    complement[index] = complement[index] if pos==1.0 else 0.0\n",
    "\n",
    "plt.plot(input, label='Truth')\n",
    "plt.plot(complement, label='Complementation')\n",
    "plt.text(20, 200, f'Day_{day_num}')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22720.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6543360/288"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Complementation</th>\n",
       "      <th>Input288</th>\n",
       "      <th>Input48</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>189.80116</td>\n",
       "      <td>80.723785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>189.66698</td>\n",
       "      <td>79.936485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>187.80453</td>\n",
       "      <td>77.447070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>184.63042</td>\n",
       "      <td>73.705890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>183.86844</td>\n",
       "      <td>68.421580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6543355</th>\n",
       "      <td>6543355</td>\n",
       "      <td>218.76384</td>\n",
       "      <td>46.619910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6543356</th>\n",
       "      <td>6543356</td>\n",
       "      <td>218.98600</td>\n",
       "      <td>45.349926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6543357</th>\n",
       "      <td>6543357</td>\n",
       "      <td>218.84352</td>\n",
       "      <td>43.624940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6543358</th>\n",
       "      <td>6543358</td>\n",
       "      <td>218.69188</td>\n",
       "      <td>42.854115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6543359</th>\n",
       "      <td>6543359</td>\n",
       "      <td>218.82568</td>\n",
       "      <td>41.545097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6543360 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Complementation   Input288  Input48  Position\n",
       "0                 0        189.80116  80.723785      0.0       0.0\n",
       "1                 1        189.66698  79.936485      0.0       0.0\n",
       "2                 2        187.80453  77.447070      0.0       0.0\n",
       "3                 3        184.63042  73.705890      0.0       0.0\n",
       "4                 4        183.86844  68.421580      0.0       0.0\n",
       "...             ...              ...        ...      ...       ...\n",
       "6543355     6543355        218.76384  46.619910      0.0       0.0\n",
       "6543356     6543356        218.98600  45.349926      0.0       0.0\n",
       "6543357     6543357        218.84352  43.624940      0.0       0.0\n",
       "6543358     6543358        218.69188  42.854115      0.0       0.0\n",
       "6543359     6543359        218.82568  41.545097      0.0       0.0\n",
       "\n",
       "[6543360 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_path = '/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/finetune/test_result_all/complementation_result/'\n",
    "file = folder_path + f'Complement_Result1_36.csv'\n",
    "df = pd.read_csv(file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq_len = 288\n",
    "day_num = 3566\n",
    "ts = np.array(df['Input288'].iloc[day_num*seq_len:day_num*seq_len+seq_len])\n",
    "pos = np.array(df['Position'].iloc[day_num*seq_len:day_num*seq_len+seq_len])\n",
    "plt.plot(ts)\n",
    "print(pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "index1 = 52\n",
    "index2 = 120\n",
    "# ts.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/data/com_test.csv')\n",
    "np.savetxt('/Users/gengyunxin/Documents/项目/traffic_model/TVTS_9.28/data/com_test.csv', ts.reshape(1,288), delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pytorch1.6.0] *",
   "language": "python",
   "name": "conda-env-pytorch1.6.0-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
